Volume 8, Issue 2
An Online Heart Rate Variability Analysis Method Based on Sliding Window Hurst Series

Taizhi Lv, Yangquan Chen & Marwin Ko

Journal of Fiber Bioengineering & Informatics, 8 (2015), pp. 391-400.

Published online: 2015-08

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  • Abstract

Heart Rate Variability (HRV) analysis is based on variability between each heartbeat which is used as a diagnosis method for assessing the cardiovascular modulation of autonomic nerve system. Up to now, most HRV analysis has been done offline. However, in many relevant applications, HRV should be analyzed online such as the analysis of stress level and the detection of the drowsiness while driving. This paper proposes an online analysis method which can be used in platforms for human robot cooperation. This online analysis method based on a sliding Hurst window can be applied to estimate the heart status. By the sliding Hurst series, the two indices, cumulative mean of Hurst series (CMHurst) and cumulative standard deviation of Hurst series (CStdHurst) are introduced as indicators to distinguish heart health status. Using this method, the hardware requirement is significantly low, and the execution time is short. Some databases from the PhysioBank are used for test these indices. The results show this method can distinguish between the groups who have normal rhythm and abnormal rhythm.

  • Keywords

Heart Rate Variability (HRV) Hurst Parameter Fractional Differintegration Sliding Window Hurst Human-robot Interaction

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COPYRIGHT: © Global Science Press

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@Article{JFBI-8-391, author = {}, title = {An Online Heart Rate Variability Analysis Method Based on Sliding Window Hurst Series}, journal = {Journal of Fiber Bioengineering and Informatics}, year = {2015}, volume = {8}, number = {2}, pages = {391--400}, abstract = {Heart Rate Variability (HRV) analysis is based on variability between each heartbeat which is used as a diagnosis method for assessing the cardiovascular modulation of autonomic nerve system. Up to now, most HRV analysis has been done offline. However, in many relevant applications, HRV should be analyzed online such as the analysis of stress level and the detection of the drowsiness while driving. This paper proposes an online analysis method which can be used in platforms for human robot cooperation. This online analysis method based on a sliding Hurst window can be applied to estimate the heart status. By the sliding Hurst series, the two indices, cumulative mean of Hurst series (CMHurst) and cumulative standard deviation of Hurst series (CStdHurst) are introduced as indicators to distinguish heart health status. Using this method, the hardware requirement is significantly low, and the execution time is short. Some databases from the PhysioBank are used for test these indices. The results show this method can distinguish between the groups who have normal rhythm and abnormal rhythm.}, issn = {2617-8699}, doi = {https://doi.org/10.3993/jfbim00130}, url = {http://global-sci.org/intro/article_detail/jfbi/4720.html} }
TY - JOUR T1 - An Online Heart Rate Variability Analysis Method Based on Sliding Window Hurst Series JO - Journal of Fiber Bioengineering and Informatics VL - 2 SP - 391 EP - 400 PY - 2015 DA - 2015/08 SN - 8 DO - http://doi.org/10.3993/jfbim00130 UR - https://global-sci.org/intro/article_detail/jfbi/4720.html KW - Heart Rate Variability (HRV) KW - Hurst Parameter KW - Fractional Differintegration KW - Sliding Window Hurst KW - Human-robot Interaction AB - Heart Rate Variability (HRV) analysis is based on variability between each heartbeat which is used as a diagnosis method for assessing the cardiovascular modulation of autonomic nerve system. Up to now, most HRV analysis has been done offline. However, in many relevant applications, HRV should be analyzed online such as the analysis of stress level and the detection of the drowsiness while driving. This paper proposes an online analysis method which can be used in platforms for human robot cooperation. This online analysis method based on a sliding Hurst window can be applied to estimate the heart status. By the sliding Hurst series, the two indices, cumulative mean of Hurst series (CMHurst) and cumulative standard deviation of Hurst series (CStdHurst) are introduced as indicators to distinguish heart health status. Using this method, the hardware requirement is significantly low, and the execution time is short. Some databases from the PhysioBank are used for test these indices. The results show this method can distinguish between the groups who have normal rhythm and abnormal rhythm.
Taizhi Lv, Yangquan Chen & Marwin Ko. (2019). An Online Heart Rate Variability Analysis Method Based on Sliding Window Hurst Series. Journal of Fiber Bioengineering and Informatics. 8 (2). 391-400. doi:10.3993/jfbim00130
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